/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.spark.examples.ml;

// $example on$

import org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary;
import org.apache.spark.ml.classification.LogisticRegression;
import org.apache.spark.ml.classification.LogisticRegressionModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.functions;
// $example off$

public class JavaLogisticRegressionSummaryExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
                .builder()
                .appName("JavaLogisticRegressionSummaryExample")
                .getOrCreate();

        // Load training data
        Dataset<Row> training = spark.read().format("libsvm")
                .load("data/mllib/sample_libsvm_data.txt");

        LogisticRegression lr = new LogisticRegression()
                .setMaxIter(10)
                .setRegParam(0.3)
                .setElasticNetParam(0.8);

        // Fit the model
        LogisticRegressionModel lrModel = lr.fit(training);

        // $example on$
        // Extract the summary from the returned LogisticRegressionModel instance trained in the earlier
        // example
        BinaryLogisticRegressionTrainingSummary trainingSummary = lrModel.binarySummary();

        // Obtain the loss per iteration.
        double[] objectiveHistory = trainingSummary.objectiveHistory();
        for (double lossPerIteration : objectiveHistory) {
            System.out.println(lossPerIteration);
        }

        // Obtain the receiver-operating characteristic as a dataframe and areaUnderROC.
        Dataset<Row> roc = trainingSummary.roc();
        roc.show();
        roc.select("FPR").show();
        System.out.println(trainingSummary.areaUnderROC());

        // Get the threshold corresponding to the maximum F-Measure and rerun LogisticRegression with
        // this selected threshold.
        Dataset<Row> fMeasure = trainingSummary.fMeasureByThreshold();
        double maxFMeasure = fMeasure.select(functions.max("F-Measure")).head().getDouble(0);
        double bestThreshold = fMeasure.where(fMeasure.col("F-Measure").equalTo(maxFMeasure))
                .select("threshold").head().getDouble(0);
        lrModel.setThreshold(bestThreshold);
        // $example off$

        spark.stop();
    }
}
